R Tutorial

An introduction to R

Introduction

This tutorial is will introduce the reader to , a free, open-source statistical computing environment often used with RStudio, a integrated development environment for .

R Project Logo

Download

Download at https://www.r-project.org/

Download RStudio at https://rstudio.com/products/rstudio/download/

Calculator

can be used as a super awesome calculator

# 5 + 3 = 8
5 + 3

## [1] 8

# 24 / (1 + 2) = 8
24 / (1 + 2)

## [1] 8

# 2 * 2 * 2 = 8
2^3

## [1] 8

# 8 * 8 = 64
sqrt(64)

## [1] 8

# -log10(0.05 / 5000000) = 8
-log10(0.05 / 5000000)

## [1] 8

Functions

has many useful built in functions

1:10

##  [1]  1  2  3  4  5  6  7  8  9 10

as.character(1:10)

##  [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10"

rep(1:2, times = 5)

##  [1] 1 2 1 2 1 2 1 2 1 2

rep(1:5, times = 2)

##  [1] 1 2 3 4 5 1 2 3 4 5

rep(1:5, each = 2)

##  [1] 1 1 2 2 3 3 4 4 5 5

rep(1:5, length.out = 7)

## [1] 1 2 3 4 5 1 2

seq(5, 50, by = 5)

##  [1]  5 10 15 20 25 30 35 40 45 50

seq(5, 50, length.out = 5)

## [1]  5.00 16.25 27.50 38.75 50.00

paste(1:10, 20:30, sep = "-")

##  [1] "1-20"  "2-21"  "3-22"  "4-23"  "5-24"  "6-25"  "7-26"  "8-27"  "9-28"  "10-29" "1-30"

paste(1:10, collapse = "-")

## [1] "1-2-3-4-5-6-7-8-9-10"

paste0("x", 1:10)

##  [1] "x1"  "x2"  "x3"  "x4"  "x5"  "x6"  "x7"  "x8"  "x9"  "x10"

min(1:10)

## [1] 1

max(1:10)

## [1] 10

range(1:10)

## [1]  1 10

mean(1:10)

## [1] 5.5

sd(1:10)

## [1] 3.02765

Custom Functions

Users can also create their own functions

customFunction1 <- function(x, y) {
  z <- 100 * x / (x + y)
  paste(z, "%")
}
customFunction1(x = 10, y = 90)

## [1] "10 %"

customFunction2 <- function(x) {
  mymin <- mean(x - sd(x))
  mymax <- mean(x) + sd(x)
  print(paste("Min =", mymin))
  print(paste("Max =", mymax))
}
customFunction2(x = 1:10)

## [1] "Min = 2.47234964590251"
## [1] "Max = 8.52765035409749"

`for` loops and `if` `else` statements

xx <- NULL #creates and empty object
for(i in 1:10) {
  xx[i] <- i*3
}
xx

##  [1]  3  6  9 12 15 18 21 24 27 30

xx %% 2 #gives the remainder when divided by 2

##  [1] 1 0 1 0 1 0 1 0 1 0

for(i in 1:length(xx)) {
  if((xx[i] %% 2) == 0) {
    print(paste(xx[i],"is Even"))
  } else { 
      print(paste(xx[i],"is Odd")) 
    }
}

## [1] "3 is Odd"
## [1] "6 is Even"
## [1] "9 is Odd"
## [1] "12 is Even"
## [1] "15 is Odd"
## [1] "18 is Even"
## [1] "21 is Odd"
## [1] "24 is Even"
## [1] "27 is Odd"
## [1] "30 is Even"

# or
ifelse(xx %% 2 == 0, "Even", "Odd")

##  [1] "Odd"  "Even" "Odd"  "Even" "Odd"  "Even" "Odd"  "Even" "Odd"  "Even"

paste(xx, ifelse(xx %% 2 == 0, "is Even", "is Odd"))

##  [1] "3 is Odd"   "6 is Even"  "9 is Odd"   "12 is Even" "15 is Odd"  "18 is Even" "21 is Odd"  "24 is Even" "27 is Odd"  "30 is Even"

Objects

Information can be stored in user defined objects, in multiple forms:

c(): a string of values
matrix(): a two dimensional matrix in one format
data.frame(): a two dimensional matrix where each column can be a different format
list():

A string…

xc <- 1:10
xc

##  [1]  1  2  3  4  5  6  7  8  9 10

xc <- c(1,2,3,4,5,6,7,8,9,10)
xc

##  [1]  1  2  3  4  5  6  7  8  9 10

A matrix…

xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = T)
xm

##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
##  [1,]    1    2    3    4    5    6    7    8    9    10
##  [2,]   11   12   13   14   15   16   17   18   19    20
##  [3,]   21   22   23   24   25   26   27   28   29    30
##  [4,]   31   32   33   34   35   36   37   38   39    40
##  [5,]   41   42   43   44   45   46   47   48   49    50
##  [6,]   51   52   53   54   55   56   57   58   59    60
##  [7,]   61   62   63   64   65   66   67   68   69    70
##  [8,]   71   72   73   74   75   76   77   78   79    80
##  [9,]   81   82   83   84   85   86   87   88   89    90
## [10,]   91   92   93   94   95   96   97   98   99   100

xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = F)
xm

##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
##  [1,]    1   11   21   31   41   51   61   71   81    91
##  [2,]    2   12   22   32   42   52   62   72   82    92
##  [3,]    3   13   23   33   43   53   63   73   83    93
##  [4,]    4   14   24   34   44   54   64   74   84    94
##  [5,]    5   15   25   35   45   55   65   75   85    95
##  [6,]    6   16   26   36   46   56   66   76   86    96
##  [7,]    7   17   27   37   47   57   67   77   87    97
##  [8,]    8   18   28   38   48   58   68   78   88    98
##  [9,]    9   19   29   39   49   59   69   79   89    99
## [10,]   10   20   30   40   50   60   70   80   90   100

A data frame…

xd <- data.frame(
  x1 = c("aa","bb","cc","dd","ee",
         "ff","gg","hh","ii","jj"),
  x2 = 1:10,
  x3 = c(1,1,1,1,1,2,2,2,3,3),
  x4 = rep(c(1,2), times = 5),
  x5 = rep(1:5, times = 2),
  x6 = rep(1:5, each = 2),
  x7 = seq(5, 50, by = 5),
  x8 = log10(1:10),
  x9 = (1:10)^3,
  x10 = c(T,T,T,F,F,T,T,F,F,F)
)
xd

##    x1 x2 x3 x4 x5 x6 x7        x8   x9   x10
## 1  aa  1  1  1  1  1  5 0.0000000    1  TRUE
## 2  bb  2  1  2  2  1 10 0.3010300    8  TRUE
## 3  cc  3  1  1  3  2 15 0.4771213   27  TRUE
## 4  dd  4  1  2  4  2 20 0.6020600   64 FALSE
## 5  ee  5  1  1  5  3 25 0.6989700  125 FALSE
## 6  ff  6  2  2  1  3 30 0.7781513  216  TRUE
## 7  gg  7  2  1  2  4 35 0.8450980  343  TRUE
## 8  hh  8  2  2  3  4 40 0.9030900  512 FALSE
## 9  ii  9  3  1  4  5 45 0.9542425  729 FALSE
## 10 jj 10  3  2  5  5 50 1.0000000 1000 FALSE

A list…

xl <- list(xc, xm, xd)
xl[[1]]

##  [1]  1  2  3  4  5  6  7  8  9 10

xl[[2]]

##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
##  [1,]    1   11   21   31   41   51   61   71   81    91
##  [2,]    2   12   22   32   42   52   62   72   82    92
##  [3,]    3   13   23   33   43   53   63   73   83    93
##  [4,]    4   14   24   34   44   54   64   74   84    94
##  [5,]    5   15   25   35   45   55   65   75   85    95
##  [6,]    6   16   26   36   46   56   66   76   86    96
##  [7,]    7   17   27   37   47   57   67   77   87    97
##  [8,]    8   18   28   38   48   58   68   78   88    98
##  [9,]    9   19   29   39   49   59   69   79   89    99
## [10,]   10   20   30   40   50   60   70   80   90   100

xl[[3]]

##    x1 x2 x3 x4 x5 x6 x7        x8   x9   x10
## 1  aa  1  1  1  1  1  5 0.0000000    1  TRUE
## 2  bb  2  1  2  2  1 10 0.3010300    8  TRUE
## 3  cc  3  1  1  3  2 15 0.4771213   27  TRUE
## 4  dd  4  1  2  4  2 20 0.6020600   64 FALSE
## 5  ee  5  1  1  5  3 25 0.6989700  125 FALSE
## 6  ff  6  2  2  1  3 30 0.7781513  216  TRUE
## 7  gg  7  2  1  2  4 35 0.8450980  343  TRUE
## 8  hh  8  2  2  3  4 40 0.9030900  512 FALSE
## 9  ii  9  3  1  4  5 45 0.9542425  729 FALSE
## 10 jj 10  3  2  5  5 50 1.0000000 1000 FALSE

Selecting Data

xc[5] # 5th element in xc

## [1] 5

xd$x3[5] # 5th element in col "x3"

## [1] 1

xd[5,"x3"] # row 5, col "x3"

## [1] 1

xd$x3 # all of col "x3"

##  [1] 1 1 1 1 1 2 2 2 3 3

xd[,"x3"] # all rows, col "x3"

##  [1] 1 1 1 1 1 2 2 2 3 3

xd[3,] # row 3, all cols

##   x1 x2 x3 x4 x5 x6 x7        x8 x9  x10
## 3 cc  3  1  1  3  2 15 0.4771213 27 TRUE

xd[c(2,4),c("x4","x5")] # rows 2 & 4, cols "x4" & "x5"

##   x4 x5
## 2  2  2
## 4  2  4

xl[[3]]$x1 # 3rd object in the list, col "x1

##  [1] "aa" "bb" "cc" "dd" "ee" "ff" "gg" "hh" "ii" "jj"

regexpr

xx <- data.frame(Name = c("Item 1 (detail 1)",
                          "Item 20 (detail 20)",
                          "Item 300 (detail 300)"),
                 Item = NA,
                 Detail = NA)
xx$Detail <- substr(xx$Name, regexpr("\\(", xx$Name)+1, regexpr("\\)", xx$Name)-1)
xx$Item <- substr(xx$Name, 1, regexpr("\\(", xx$Name)-2)
xx

##                    Name     Item     Detail
## 1     Item 1 (detail 1)   Item 1   detail 1
## 2   Item 20 (detail 20)  Item 20  detail 20
## 3 Item 300 (detail 300) Item 300 detail 300

Data Formats

Data can also be saved in many formats:

numeric
integer
character
factor
logical

xd$x3 <- as.character(xd$x3)
xd$x3

##  [1] "1" "1" "1" "1" "1" "2" "2" "2" "3" "3"

xd$x3 <- as.numeric(xd$x3)
xd$x3

##  [1] 1 1 1 1 1 2 2 2 3 3

xd$x3 <- as.factor(xd$x3)
xd$x3

##  [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 1 2 3

xd$x3 <- factor(xd$x3, levels = c("3","2","1"))
xd$x3

##  [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 3 2 1

xd$x10

##  [1]  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE

as.numeric(xd$x10) # TRUE = 1, FALSE = 0

##  [1] 1 1 1 0 0 1 1 0 0 0

sum(xd$x10)

## [1] 5

Internal structure of an object can be checked with str()

str(xc) # c()

##  num [1:10] 1 2 3 4 5 6 7 8 9 10

str(xm) # matrix()

##  int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...

str(xd) # data.frame()

## 'data.frame':    10 obs. of  10 variables:
##  $ x1 : chr  "aa" "bb" "cc" "dd" ...
##  $ x2 : int  1 2 3 4 5 6 7 8 9 10
##  $ x3 : Factor w/ 3 levels "3","2","1": 3 3 3 3 3 2 2 2 1 1
##  $ x4 : num  1 2 1 2 1 2 1 2 1 2
##  $ x5 : int  1 2 3 4 5 1 2 3 4 5
##  $ x6 : int  1 1 2 2 3 3 4 4 5 5
##  $ x7 : num  5 10 15 20 25 30 35 40 45 50
##  $ x8 : num  0 0.301 0.477 0.602 0.699 ...
##  $ x9 : num  1 8 27 64 125 216 343 512 729 1000
##  $ x10: logi  TRUE TRUE TRUE FALSE FALSE TRUE ...

str(xl) # list()

## List of 3
##  $ : num [1:10] 1 2 3 4 5 6 7 8 9 10
##  $ : int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...
##  $ :'data.frame':    10 obs. of  10 variables:
##   ..$ x1 : chr [1:10] "aa" "bb" "cc" "dd" ...
##   ..$ x2 : int [1:10] 1 2 3 4 5 6 7 8 9 10
##   ..$ x3 : num [1:10] 1 1 1 1 1 2 2 2 3 3
##   ..$ x4 : num [1:10] 1 2 1 2 1 2 1 2 1 2
##   ..$ x5 : int [1:10] 1 2 3 4 5 1 2 3 4 5
##   ..$ x6 : int [1:10] 1 1 2 2 3 3 4 4 5 5
##   ..$ x7 : num [1:10] 5 10 15 20 25 30 35 40 45 50
##   ..$ x8 : num [1:10] 0 0.301 0.477 0.602 0.699 ...
##   ..$ x9 : num [1:10] 1 8 27 64 125 216 343 512 729 1000
##   ..$ x10: logi [1:10] TRUE TRUE TRUE FALSE FALSE TRUE ...

Packages

Additional libraries can be installed and loaded for use.

install.packages("scales")

library(scales)
xx <- data.frame(Values = 1:10)
xx$Rescaled <- rescale(x = xx$Values, to = c(1,30))
xx

##    Values  Rescaled
## 1       1  1.000000
## 2       2  4.222222
## 3       3  7.444444
## 4       4 10.666667
## 5       5 13.888889
## 6       6 17.111111
## 7       7 20.333333
## 8       8 23.555556
## 9       9 26.777778
## 10     10 30.000000

libraries can also be used without having to load them

scales::rescale(1:10, to = c(1,30))

##  [1]  1.000000  4.222222  7.444444 10.666667 13.888889 17.111111 20.333333 23.555556 26.777778 30.000000

Data Wrangling

R for Data Science - https://r4ds.had.co.nz/

xx <- data.frame(Group = c("X","X","Y","Y","Y","X","X","X","Y","Y"),
                 Data1 = 1:10, 
                 Data2 = seq(10, 100, by = 10))
xx$NewData1 <- xx$Data1 + xx$Data2
xx$NewData2 <- xx$Data1 * 1000
xx

##    Group Data1 Data2 NewData1 NewData2
## 1      X     1    10       11     1000
## 2      X     2    20       22     2000
## 3      Y     3    30       33     3000
## 4      Y     4    40       44     4000
## 5      Y     5    50       55     5000
## 6      X     6    60       66     6000
## 7      X     7    70       77     7000
## 8      X     8    80       88     8000
## 9      Y     9    90       99     9000
## 10     Y    10   100      110    10000

xx$Data1 < 5 # which are less than 5

##  [1]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE

xx[xx$Data1 < 5,]

##   Group Data1 Data2 NewData1 NewData2
## 1     X     1    10       11     1000
## 2     X     2    20       22     2000
## 3     Y     3    30       33     3000
## 4     Y     4    40       44     4000

xx[xx$Group == "X", c("Group","Data2","NewData1")]

##   Group Data2 NewData1
## 1     X    10       11
## 2     X    20       22
## 6     X    60       66
## 7     X    70       77
## 8     X    80       88

Data wrangling with tidyverse and pipes (%>%)

library(tidyverse) # install.packages("tidyverse")
xx <- data.frame(Group = c("X","X","Y","Y","Y","Y","Y","X","X","X")) %>%
  mutate(Data1 = 1:10, 
         Data2 = seq(10, 100, by = 10),
         NewData1 = Data1 + Data2,
         NewData2 = Data1 * 1000)
xx

##    Group Data1 Data2 NewData1 NewData2
## 1      X     1    10       11     1000
## 2      X     2    20       22     2000
## 3      Y     3    30       33     3000
## 4      Y     4    40       44     4000
## 5      Y     5    50       55     5000
## 6      Y     6    60       66     6000
## 7      Y     7    70       77     7000
## 8      X     8    80       88     8000
## 9      X     9    90       99     9000
## 10     X    10   100      110    10000

filter(xx, Data1 < 5)

##   Group Data1 Data2 NewData1 NewData2
## 1     X     1    10       11     1000
## 2     X     2    20       22     2000
## 3     Y     3    30       33     3000
## 4     Y     4    40       44     4000

xx %>% filter(Data1 < 5)

##   Group Data1 Data2 NewData1 NewData2
## 1     X     1    10       11     1000
## 2     X     2    20       22     2000
## 3     Y     3    30       33     3000
## 4     Y     4    40       44     4000

xx %>% filter(Group == "X") %>% 
  select(Group, NewColName=Data2, NewData1)

##   Group NewColName NewData1
## 1     X         10       11
## 2     X         20       22
## 3     X         80       88
## 4     X         90       99
## 5     X        100      110

xs <- xx %>% 
  group_by(Group) %>% 
  summarise(Data2_mean = mean(Data2),
            Data2_sd = sd(Data2),
            NewData2_mean = mean(NewData2),
            NewData2_sd = sd(NewData2))
xs

## # A tibble: 2 × 5
##   Group Data2_mean Data2_sd NewData2_mean NewData2_sd
##   <chr>      <dbl>    <dbl>         <dbl>       <dbl>
## 1 X             60     41.8          6000       4183.
## 2 Y             50     15.8          5000       1581.

xx %>% left_join(xs, by = "Group")

##    Group Data1 Data2 NewData1 NewData2 Data2_mean Data2_sd NewData2_mean NewData2_sd
## 1      X     1    10       11     1000         60 41.83300          6000    4183.300
## 2      X     2    20       22     2000         60 41.83300          6000    4183.300
## 3      Y     3    30       33     3000         50 15.81139          5000    1581.139
## 4      Y     4    40       44     4000         50 15.81139          5000    1581.139
## 5      Y     5    50       55     5000         50 15.81139          5000    1581.139
## 6      Y     6    60       66     6000         50 15.81139          5000    1581.139
## 7      Y     7    70       77     7000         50 15.81139          5000    1581.139
## 8      X     8    80       88     8000         60 41.83300          6000    4183.300
## 9      X     9    90       99     9000         60 41.83300          6000    4183.300
## 10     X    10   100      110    10000         60 41.83300          6000    4183.300

Read/Write data

xx <- read.csv("data_r_tutorial.csv")
write.csv(xx, "data_r_tutorial.csv", row.names = F)

For excel sheets, the package readxl can be used to read in sheets of data.

library(readxl) # install.packages("readxl")
xx <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data")

Tidy Data

Tutorial 1 - https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html

Tutorial 2 - https://r4ds.had.co.nz/tidy-data.html

yy <- xx %>%
  group_by(Name, Location) %>%
  summarise(Mean_DTF = round(mean(DTF),1)) %>% 
  arrange(Location)
yy

## # A tibble: 9 × 3
## # Groups:   Name [3]
##   Name          Location            Mean_DTF
##   <chr>         <chr>                  <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh     86.7
## 2 ILL 618 AGL   Jessore, Bangladesh     79.3
## 3 Laird AGL     Jessore, Bangladesh     76.8
## 4 CDC Maxim AGL Metaponto, Italy       134. 
## 5 ILL 618 AGL   Metaponto, Italy       138. 
## 6 Laird AGL     Metaponto, Italy       137. 
## 7 CDC Maxim AGL Saskatoon, Canada       52.5
## 8 ILL 618 AGL   Saskatoon, Canada       47  
## 9 Laird AGL     Saskatoon, Canada       56.8

yy <- yy %>% spread(key = Location, value = Mean_DTF)
yy

## # A tibble: 3 × 4
## # Groups:   Name [3]
##   Name          `Jessore, Bangladesh` `Metaponto, Italy` `Saskatoon, Canada`
##   <chr>                         <dbl>              <dbl>               <dbl>
## 1 CDC Maxim AGL                  86.7               134.                52.5
## 2 ILL 618 AGL                    79.3               138.                47  
## 3 Laird AGL                      76.8               137.                56.8

yy <- yy %>% gather(key = TraitName, value = Value, 2:4)
yy

## # A tibble: 9 × 3
## # Groups:   Name [3]
##   Name          TraitName           Value
##   <chr>         <chr>               <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh  86.7
## 2 ILL 618 AGL   Jessore, Bangladesh  79.3
## 3 Laird AGL     Jessore, Bangladesh  76.8
## 4 CDC Maxim AGL Metaponto, Italy    134. 
## 5 ILL 618 AGL   Metaponto, Italy    138. 
## 6 Laird AGL     Metaponto, Italy    137. 
## 7 CDC Maxim AGL Saskatoon, Canada    52.5
## 8 ILL 618 AGL   Saskatoon, Canada    47  
## 9 Laird AGL     Saskatoon, Canada    56.8

yy <- yy %>% spread(key = Name, value = Value)
yy

## # A tibble: 3 × 4
##   TraitName           `CDC Maxim AGL` `ILL 618 AGL` `Laird AGL`
##   <chr>                         <dbl>         <dbl>       <dbl>
## 1 Jessore, Bangladesh            86.7          79.3        76.8
## 2 Metaponto, Italy              134.          138.        137. 
## 3 Saskatoon, Canada              52.5          47          56.8

Base Plotting

We will start with some basic plotting using the base function plot()

Tutorial 1 - http://www.sthda.com/english/wiki/r-base-graphs

Tutorial 2 - https://bookdown.org/rdpeng/exdata/the-base-plotting-system-1.html

# A basic scatter plot
plot(x = xd$x8, y = xd$x9)

# Adjust color and shape of the points
plot(x = xd$x8, y = xd$x9, col = "darkred", pch = 0)

plot(x = xd$x8, y = xd$x9, col = xd$x4, pch = xd$x4)

# Adjust plot type 
plot(x = xd$x8, y = xd$x9, type = "line")

# Adjust linetype
plot(x = xd$x8, y = xd$x9, type = "line", lty = 2)

# Plot lines and points
plot(x = xd$x8, y = xd$x9, type = "both")

Now lets create some random and normally distributed data to make some more complicated plots

# 100 random uniformly distributed numbers ranging from 0 - 100
ru <- runif(100, min = 0, max = 100)
ru

##   [1] 66.2047307 12.5106280 86.5889802 53.7621643 24.4316097 49.0163064 19.4064194 95.8560112 99.5807469 77.3647773 32.3739522  4.8520115 51.9533308
##  [14] 53.6998051 36.9585262  8.6916532 42.3224789  6.7516913 14.0675613 89.7331792 42.3544492 97.4565332 30.5991179 14.0958920 74.9625834 70.6976288
##  [27] 37.8278640 95.9698924 68.5683610 72.0550259 66.2311552 41.6114787 23.3320478 26.1104584 24.8710959  6.6230036 46.1706153 21.7189687  4.0826290
##  [40] 43.6162797 63.4044853  4.2103461 39.2501964 36.7747618 96.5590203 25.3556544 53.9584011 25.2169715 40.7925422  9.9359228 75.2218464 28.5622515
##  [53] 69.7050691 57.8728163 73.6126436 74.3549648 30.8210045 80.0911954 36.5670830 55.4318829 13.5484764 71.0079963 29.4771229 55.7814691 29.6723413
##  [66] 72.3760708  2.0920070 51.8587593 56.8562507 56.3754774 46.8610252 82.7894266  0.4903728 90.5678804 45.0051999  4.7083384 96.0515993 24.0149923
##  [79] 59.2474991 18.4830467 26.5017679 61.8837270 69.3358276 71.0520657 64.8142861 16.3886072 32.4340469 10.1513888 72.2470621  2.2430737 16.7136953
##  [92] 38.2518585  2.4210110 26.7435775 90.5573979 87.0539967 19.9952721 89.5469402 74.3502848 17.6681502

plot(x = ru)

order(ru)

##   [1]  73  67  90  93  39  42  76  12  36  18  16  50  88   2  61  19  24  86  91 100  80   7  97  38  33  78   5  35  48  46  34  81  94  52  63  65  23
##  [38]  57  11  87  59  44  15  27  92  43  49  32  17  21  40  75  37  71   6  68  13  14   4  47  60  64  70  69  54  79  82  41  85   1  31  29  83  53
##  [75]  26  62  84  30  89  66  55  99  56  25  51  10  58  72   3  96  98  20  95  74   8  28  77  45  22   9

ru<- ru[order(ru)]
ru

##   [1]  0.4903728  2.0920070  2.2430737  2.4210110  4.0826290  4.2103461  4.7083384  4.8520115  6.6230036  6.7516913  8.6916532  9.9359228 10.1513888
##  [14] 12.5106280 13.5484764 14.0675613 14.0958920 16.3886072 16.7136953 17.6681502 18.4830467 19.4064194 19.9952721 21.7189687 23.3320478 24.0149923
##  [27] 24.4316097 24.8710959 25.2169715 25.3556544 26.1104584 26.5017679 26.7435775 28.5622515 29.4771229 29.6723413 30.5991179 30.8210045 32.3739522
##  [40] 32.4340469 36.5670830 36.7747618 36.9585262 37.8278640 38.2518585 39.2501964 40.7925422 41.6114787 42.3224789 42.3544492 43.6162797 45.0051999
##  [53] 46.1706153 46.8610252 49.0163064 51.8587593 51.9533308 53.6998051 53.7621643 53.9584011 55.4318829 55.7814691 56.3754774 56.8562507 57.8728163
##  [66] 59.2474991 61.8837270 63.4044853 64.8142861 66.2047307 66.2311552 68.5683610 69.3358276 69.7050691 70.6976288 71.0079963 71.0520657 72.0550259
##  [79] 72.2470621 72.3760708 73.6126436 74.3502848 74.3549648 74.9625834 75.2218464 77.3647773 80.0911954 82.7894266 86.5889802 87.0539967 89.5469402
##  [92] 89.7331792 90.5573979 90.5678804 95.8560112 95.9698924 96.0515993 96.5590203 97.4565332 99.5807469

plot(x = ru)

# 100 normally distributed numbers with a mean of 50 and sd of 10
nd <- rnorm(100, mean = 50, sd = 10)
nd

##   [1] 46.89494 61.60018 56.29758 66.28115 44.23045 65.12512 33.51570 42.62608 55.23005 59.81862 59.67832 44.81626 80.15640 55.23955 53.16301 45.17778
##  [17] 38.80791 44.43525 55.81562 47.50522 43.89160 39.17035 54.50052 46.74800 48.15617 45.19570 59.72816 41.55305 35.37083 50.68256 52.41494 27.46417
##  [33] 46.88116 58.04001 47.25352 79.77435 55.44750 40.09815 41.43764 42.92237 45.28325 51.84488 53.20476 52.75069 48.48228 52.16668 61.81757 44.65777
##  [49] 54.38317 69.47525 55.12099 46.17629 52.71279 37.69611 70.28738 51.06791 52.06192 54.56166 53.62279 38.91093 46.03735 40.45575 46.47280 69.63814
##  [65] 45.08613 49.93161 52.71545 46.54619 67.34302 54.37465 22.82741 57.44276 52.56324 46.64720 50.03854 52.07791 48.47559 52.43605 46.86132 42.41445
##  [81] 54.66657 31.26410 62.43206 31.52069 52.01730 52.43801 44.79937 50.94720 40.56076 48.39943 42.64030 52.66114 31.97951 59.12295 50.49402 49.09055
##  [97] 52.55578 42.08979 43.04400 54.34249

nd <- nd[order(nd)]
nd

##   [1] 22.82741 27.46417 31.26410 31.52069 31.97951 33.51570 35.37083 37.69611 38.80791 38.91093 39.17035 40.09815 40.45575 40.56076 41.43764 41.55305
##  [17] 42.08979 42.41445 42.62608 42.64030 42.92237 43.04400 43.89160 44.23045 44.43525 44.65777 44.79937 44.81626 45.08613 45.17778 45.19570 45.28325
##  [33] 46.03735 46.17629 46.47280 46.54619 46.64720 46.74800 46.86132 46.88116 46.89494 47.25352 47.50522 48.15617 48.39943 48.47559 48.48228 49.09055
##  [49] 49.93161 50.03854 50.49402 50.68256 50.94720 51.06791 51.84488 52.01730 52.06192 52.07791 52.16668 52.41494 52.43605 52.43801 52.55578 52.56324
##  [65] 52.66114 52.71279 52.71545 52.75069 53.16301 53.20476 53.62279 54.34249 54.37465 54.38317 54.50052 54.56166 54.66657 55.12099 55.23005 55.23955
##  [81] 55.44750 55.81562 56.29758 57.44276 58.04001 59.12295 59.67832 59.72816 59.81862 61.60018 61.81757 62.43206 65.12512 66.28115 67.34302 69.47525
##  [97] 69.63814 70.28738 79.77435 80.15640

plot(x = nd)

hist(x = nd)

hist(nd, breaks = 20, col = "darkgreen")

plot(x = density(nd))

boxplot(x = nd)

boxplot(x = nd, horizontal = T)

ggplot2

Lets be honest, the base plots are ugly! The ggplot2 package gives the user to create a better, more visually appealing plots. Additional packages such as ggbeeswarm and ggrepel also contain useful functions to add to the functionality of ggplot2.

ggplot2 - https://ggplot2.tidyverse.org/

Tutorial 1 - http://r-statistics.co/ggplot2-Tutorial-With-R.html

Tutorial 2 - https://www.statsandr.com/blog/graphics-in-r-with-ggplot2/

The R Graph Gallery - https://www.r-graph-gallery.com/ggplot2-package.html

library(ggplot2)
mp <- ggplot(xd, aes(x = x8, y = x9))
mp + geom_point()

mp + geom_point(aes(color = x3, shape = x3), size = 4)

mp + geom_line(size = 2)

mp + geom_line(aes(color = x3), size = 2)

mp + geom_smooth(method = "loess")

mp + geom_smooth(method = "lm")

xx <- data.frame(data = c(rnorm(50, mean = 40, sd = 10),
                          rnorm(50, mean = 60, sd = 5)),
                 group = factor(rep(1:2, each = 50)),
                 label = c("Label1", rep(NA, 49), "Label2", rep(NA, 49)))
mp <- ggplot(xx, aes(x = data, fill = group))
mp + geom_histogram(color = "black")

mp + geom_histogram(color = "black", position = "dodge")

mp1 <- mp + geom_histogram(color = "black") + facet_grid(group~.)
mp1

mp + geom_density(alpha = 0.5)

mp <- ggplot(xx, aes(x = group, y = data, fill = group))
mp + geom_boxplot(color = "black")

mp + geom_boxplot() + geom_point()

mp + geom_violin() + geom_boxplot(width = 0.1, fill = "white")

library(ggbeeswarm)
mp + geom_quasirandom()

mp + geom_quasirandom(aes(shape = group))

mp2 <- mp + geom_violin() + 
  geom_boxplot(width = 0.1, fill = "white") +
  geom_beeswarm(alpha = 0.5)
library(ggrepel)
mp2 + geom_text_repel(aes(label = label), nudge_x = 0.4)

library(ggpubr)
ggarrange(mp1, mp2, ncol = 2, widths = c(2,1),
          common.legend = T, legend = "bottom")

Statistics

Handbook of Biological Statistics - http://biostathandbook.com/

R Companion for ^ - https://rcompanion.org/rcompanion/a_02.html

# Prep data
lev_Loc  <- c("Saskatoon, Canada", "Jessore, Bangladesh", "Metaponto, Italy")
lev_Name <- c("ILL 618 AGL", "CDC Maxim AGL", "Laird AGL")
dd <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data") %>%
  mutate(Location = factor(Location, levels = lev_Loc),
         Name = factor(Name, levels = lev_Name))
xx <- dd %>%
  group_by(Name, Location) %>%
  summarise(Mean_DTF = mean(DTF))
xx %>% spread(Location, Mean_DTF)

## # A tibble: 3 × 4
## # Groups:   Name [3]
##   Name          `Saskatoon, Canada` `Jessore, Bangladesh` `Metaponto, Italy`
##   <fct>                       <dbl>                 <dbl>              <dbl>
## 1 ILL 618 AGL                  47                    79.3               138.
## 2 CDC Maxim AGL                52.5                  86.7               134.
## 3 Laird AGL                    56.8                  76.8               137.

# Plot
mp1 <- ggplot(dd, aes(x = Location, y = DTF, color = Name, shape = Name)) +
  geom_point(size = 2, alpha = 0.7, position = position_dodge(width=0.5))
mp2 <- ggplot(xx, aes(x = Location, y = Mean_DTF, 
                      color = Name, group = Name, shape = Name)) +
  geom_point(size = 2.5, alpha = 0.7) + 
  geom_line(size = 1, alpha = 0.7) +
  theme(legend.position = "top")
ggarrange(mp1, mp2, ncol = 2, common.legend = T, legend = "top")

From first glace, it is clear there are differences between genotypes, locations, and genotype x environment (GxE) interactions. Now let’s do a few statistical tests.

summary(aov(DTF ~ Name * Location, data = dd))

##               Df Sum Sq Mean Sq  F value   Pr(>F)    
## Name           2     88      44    3.476   0.0395 *  
## Location       2  65863   32931 2598.336  < 2e-16 ***
## Name:Location  4    560     140   11.044 2.52e-06 ***
## Residuals     45    570      13                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

As expected, an ANOVA shows statistical significance for genotype (p-value = 0.0395), Location (p-value < 2e-16) and GxE interactions (p-value < 2.52e-06). However, all this tells us is that one genotype is different from the rest, one location is different from the others and that there is GxE interactions. If we want to be more specific, would need to do some multiple comparison tests.

If we only have two things to compare, we could do a t-test.

xx <- dd %>% 
  filter(Location %in% c("Saskatoon, Canada", "Jessore, Bangladesh")) %>%
  spread(Location, DTF)
t.test(x = xx$`Saskatoon, Canada`, y = xx$`Jessore, Bangladesh`)

## 
##  Welch Two Sample t-test
## 
## data:  xx$`Saskatoon, Canada` and xx$`Jessore, Bangladesh`
## t = -17.521, df = 32.701, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -32.18265 -25.48402
## sample estimates:
## mean of x mean of y 
##  52.11111  80.94444

DTF in Saskatoon, Canada is significantly different (p-value < 2.2e-16) from DTF in Jessore, Bangladesh.

xx <- dd %>% 
  filter(Name %in% c("ILL 618 AGL", "Laird AGL"),
         Location == "Metaponto, Italy") %>%
  spread(Name, DTF)
t.test(x = xx$`ILL 618 AGL`, y = xx$`Laird AGL`)

## 
##  Welch Two Sample t-test
## 
## data:  xx$`ILL 618 AGL` and xx$`Laird AGL`
## t = 0.38008, df = 8.0564, p-value = 0.7137
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -5.059739  7.059739
## sample estimates:
## mean of x mean of y 
##  137.8333  136.8333

DTF between ILL 618 AGL and Laird AGL are not significantly different (p-value = 0.7137) in Metaponto, Italy.

pch Plot

xx <- data.frame(x = rep(1:6, times = 5, length.out = 26),
                 y = rep(5:1, each = 6, length.out = 26),
                 pch = 0:25)
mp <- ggplot(xx, aes(x = x, y = y, shape = as.factor(pch))) +
  geom_point(color = "darkred", fill = "darkblue", size = 5) +
  geom_text(aes(label = pch), nudge_x = -0.25) +
  scale_shape_manual(values = xx$pch) +
  scale_x_continuous(breaks = 6:1) +
  scale_y_continuous(breaks = 6:1) +
  theme_void() +
  theme(legend.position = "none",
        plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5),
        axis.text = element_blank(),
        axis.ticks = element_blank()) +
  labs(title = "Plot symbols in R (pch)",
       subtitle = "color = \"darkred\", fill = \"darkblue\"",
       x = NULL, y = NULL)
ggsave("pch.png", mp, width = 4.5, height = 3, bg = "white")

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